The field of the invention is a method and system for assessing the operation of imaging systems and, in particular, a method and system for automation-assisted assessment of the operational characteristics of, for example, a magnetic resonance imaging (MRI) system.
In a magnetic resonance imaging (MRI) system, when a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and operating near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped” into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited nuclei or “spins” after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
Periodically, MRI and other medical imaging devices require testing to ensure that they meet certain performance specifications. Because the resolution and accuracy of the machines may change over time, without these routine inspections the machines could begin generating images that do not provide sufficient detail or accuracy to make useful diagnoses. To maintain certification of MRI devices, therefore, a site may establish a weekly quality control (QC) protocol. Generally, the QC protocols require that the machine being certified scan a number of phantoms that have known structures and configurations. By comparing the images of the phantoms captured by the device with the known structure of the phantoms themselves, it is possible to evaluate, analyze, and tune the performance of the imaging device or otherwise evaluate an operational characteristic of the device.
Phantoms can be manufactured using various materials such as aqueous paramagnetic solutions; pure gels of gelatin, agar, polyvinyl alcohol, silicone, polyacrylamide, or agarose; organic doped gels; paramagnetically doped gels; and reverse micelle solutions. The materials are generally selected for their detectability by the particular imaging device to be certified. In each phantom, the materials are formed into well-defined structures. Multiple phantoms, each having different structures and incorporating different materials may make-up a particular QC protocol configured to test many characteristics of a particular imaging device.
In the case of MRI devices, one QC protocol requires the imaging and analysis of eight separate image quality metrics. Because the compliance process requires manual calculation and analysis of each of the eight image quality metrics, the process can be time consuming and prone to human error. Furthermore, as the compliance processes and associated phantoms are updated and modified in view of upgrades in MRI technology, it is necessary to continually update the associated QC processes and analysis procedures. If all of the required QC protocols are implemented by humans, the possibility of human error may increase substantially.
For current MRI devices, a Low Contrast Detectability (LCD) test has been developed. The LCD test assesses the extent to which objects of low-contrast are discernible in four separate axial slices. FIG. 1 is an illustration of an example LCD resolution pattern generated after scanning a phantom object such as an LCD phantom. In each slice of the phantom (FIG. 1 illustrates a view taken through a single slice of the phantom), the low contrast objects appear as rows 4 of small disks 6, with each row radiating from the center of a circle as in spokes of a wheel as shown in FIG. 1. The contrast levels are the same in each slice and decrease in slice order throughout the phantom. The spoke count starts with the first spoke having the largest diameter disks, and rotates clockwise until a spoke is reached where one or more of the disks are not discernible from the background. The number of complete spokes detected or successfully imaged is the score for a particular slice. FIG. 1 shows an example phantom, but other phantoms having different configurations of disks, or alternative shapes in place of the disks may also be used for LCD testing.
LCD resolution patterns (such as that shown in FIG. 1) are important tests for the verification of diagnostic image quality generated by a medical imaging device. Although the human visual system is extremely sensitive to the detection of low contrast objects, there is substantial room for subjectivity in analyzing the images. Furthermore, due to the need to discern important information from subtle differences in the images, automation of the LCD tests by computer analysis is difficult. As a result, trained or expert observers perform the LCD tests requiring time consuming manual intervention and interaction.
Accordingly, there is a need for systems and methods to reduce the regular burden of performing analysis of low contrast resolution detection tests.